Investigating the Behavior of Compact Composite Descriptors in Early Fusion, Late Fusion and Distributed Image Retrieval

In Content-Based Image Retrieval (CBIR) systems, the visual content of the images is mapped into a new space named the feature space. The features that are chosen must be discriminative and sufficient for the description of the objects. The key to attaining a successful retrieval system is to choose the right features that represent the images as unique as possible. A feature is a set of characteristics of the image, such as color, texture, and shape. In addition, a feature can be enriched with information about the spatial distribution of the characteristic that it describes. Evaluation of the performance of low-level features is usually done on homogenous benchmarking databases with a limited number of images. In real-world image retrieval systems, databases have a much larger scale and may be heterogeneous. This paper investigates the behavior of Compact Composite Descriptors (CCDs) on heterogeneous databases of a larger scale. Early and late fusion techniques are tested and their performance in distributed image retrieval is calculated. This study demonstrates that, even if it is not possible to overcome the semantic gap in image retrieval by feature similarity, it is still possible to increase the retrieval effectiveness

An Information Theoretic Approach to Content Based Image Retrieval.

We propose an information theoretic approach to the representation and comparison of color features in digital images to handle various problems in the area of content-based image retrieval. The interpretation of color histograms as joint probability density functions enables the use of a wide range of concepts from information theory to be considered in the extraction of color features from images and the computation of similarity between pairs of images. The entropy of an image is a measure of the randomness of the color distribution in an image. Rather than replacing color histograms as an image representation, we demonstrate that image entropy can be used to augment color histograms for more efficient image retrieval. We propose an indexing algorithm in which image entropy is used to drastically reduce the search space for color histogram computations. Our experimental tests applied to an image database with 10,000 images suggest that the image entropy-based indexing algorithm is scalable for image retrieval of large image databases. We also proposed a new similarity measure called the maximum relative entropy measure for comparing image feature vectors that represent probability density functions. This measure is an improvement of the Kullback-Leibler number in that it is non-negative and satisfies the identity and symmetry axioms. We also propose a new usability paradigm called Query By Example Sets (QBES) that allows users, particularly novice users, the ability to express queries in terms of multiple images

Content-based Image Retrieval using color models and linear discriminant analysis

The past few years have seen a major development in Content-based Image Retrieval (CBIR) due to the needs by various fields in accessing visual data, particularly images. As a result, several techniques have been developed to allow image databases to be queried by their image content. Color Models is one of the promising color descriptors used to extract and index image features effectively. However, the conventional Color Models and its advancements are not able to accurately capture the global color information and derive high-level semantic concepts from low-level image features for better image understanding. A new method for CBIR has been introduced by integrating the Color Models with Linear Discriminant Analysis (LDA) where the proposed method not only able to provide better representation for low-level feature but also allow optimal linear transformation to be found which projects the color coefficients into a low-dimensional space. The Hue-Saturation-Value (HSV) is first extracted from an image followed by the implementation of the Co-occurrence Matrix on the extracted color pixels. LDA is then performed to classify the generated low-dimensional color features of an image and its respective semantic labelling according to classes. Retrieval experiments conducted on 1000 SIMPLIcity image database has demonstrated that the proposed method has achieved a significant improvement in effectiveness compared to the benchmark method

Content-based Image Retrieval using color models and linear discriminant analysis

The past few years have seen a major development in Content-based Image Retrieval (CBIR) due to the needs by various fields in accessing visual data, particularly images. As a result, several techniques have been developed to allow image databases to be queried by their image content. Color Models is one of the promising color descriptors used to extract and index image features effectively. However, the conventional Color Models and its advancements are not able to accurately capture the global color information and derive high-level semantic concepts from low-level image features for better image understanding. A new method for CBIR has been introduced by integrating the Color Models with Linear Discriminant Analysis (LDA) where the proposed method not only able to provide better representation for low-level feature but also allow optimal linear transformation to be found which projects the color coefficients into a low-dimensional space. The Hue-Saturation-Value (HSV) is first extracted from an image followed by the implementation of the Co-occurrence Matrix on the extracted color pixels. LDA is then performed to classify the generated low-dimensional color features of an image and its respective semantic labelling according to classes. Retrieval experiments conducted on 1000 SIMPLIcity image database has demonstrated that the proposed method has achieved a significant improvement in effectiveness compared to the benchmark method

Content Based Image Retrieval Based on Shape, Color and Structure of the Image

In the recent era, as technology is growing rapidly the usage of social media is also increasing as a result large databases are required for storing the images. With the advancements in the technology, the storage of these images in computers has become possible. But retrieving the images is becoming a big task. We need to store them in a sequential manner and retrieve them when required. This paper details retrieval of images by considering the features related to content like shape, color, texture is called CBIR (content based image retrieval). As it is very difficult to extract the pictures in such huge data bases so we chose this technique which aim at high efficiency

A case for image quering through image spots

We present an image spot query technique as an alternative for content-based image retrieval based on similarity over feature vectors. Image spots are selective parts of a query image designated by users as highly relevant for the desired answer set. Compared to traditional approaches, our technique allows users to search image databases for local (spatial, color and color transition) characteristics rather than global features. When a user query is presented to our search engine, the engine does not impose any (similarity, ranking, cutoff) policy of its own on the answer set; it performs an exact match based on the query terms against the database. Semantic higher concepts such as weighing the relevance of query terms, is left to the user as a task while refining their query to reach the desired answer set. Given the hundreds of feature terms involved in query spots, refinement algorithms are to be encapsulated in separate applications, which act as an intermediary between our search engine and the users

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining